基于硬件实现的图灵机的设计与实验表征及其自动卡片式编程
Design and Empirical Characterization of a Hardware-Realized Turing Machine with Automated Card-Based Programming
- Thapathali Campus（塔帕塔利校区）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文设计并实现了一种具备自主执行、可重编程光学输入功能的硬件图灵机，通过优化穿孔卡片解码算法提升了准确率，经实验验证其机械与计算性能符合要求。
AI中文摘要：
图灵机的物理实现仍然很少见，现有的机电演示器和机械逻辑游戏通常需要操作员手动干预，以触发每个计算步骤或重新配置状态表，或同时进行这两种操作。这将先前的物理模型限制为短的、操作员控制节奏的演示，并且无法自主执行扩展计算。本文针对这一差距，提出了一种硬件图灵机，该图灵机能够自主多步骤执行和可重新编程的光学输入，在程序之间无需手动干预。该系统集成了Arduino Mega用于状态转换逻辑，双NEMA 17步进电机用于双向纸带驱动，红外反射传感器用于符号检测，以及基于ESP32-CAM的光学穿孔卡片读取器用于自动状态表加载。由于ESP32-CAM微控制器环境的内存和库限制，非均匀照明下的孔洞检测采用了带有局部自适应阈值的广度优先搜索洪水填充算法，而非固定全局阈值；这在20张卡片的测试集上将卡片解码准确率从75%提高到90%（若采用机械卡片整平则达到100%）。机械评估显示制造精度为±0.15 mm，齿条齿轮传动在50次试验中的位置误差低于0.3 mm，满系统负载下的电压供应稳定性在±0.2 V以内。通过与并行软件模拟器tlang进行端到端计算验证，所有硬件输出在多个测试程序中均与模拟参考完全匹配。该系统通过自主执行、可重新编程的光学输入，以及对其机械、光学和计算性能的定量评估，推进了先前的物理图灵机演示。
英文摘要：
Physical implementations of Turing Machines remain rare, and existing electromechanical demonstrators and mechanical logic games typically require manual operator intervention, either to trigger each computational step or to reconfigure the state table, or both. This restricts prior physical models to short, operator-paced demonstrations and prevents autonomous execution of extended computations. This paper addresses that gap with a hardware Turing Machine that enables autonomous multi-step execution and reprogrammable optical input without manual intervention between programs. The system integrates an Arduino Mega for state-transition logic, dual NEMA 17 stepper motors for bidirectional tape actuation, infrared reflectance sensors for symbol detection, and an ESP32-CAM-based optical punched-card reader for automated state-table loading. Hole detection under non-uniform illumination used a Breadth-First Search flood-fill algorithm with local adaptive thresholding rather than fixed global thresholding, driven by the memory and library constraints of the ESP32-CAM's microcontroller environment; this improved card-decoding accuracy from 75% to 90% (100% with mechanical card flattening) on a 20-card test set. Mechanical evaluation showed fabrication accuracy of +/-0.15 mm, rack-and-pinion positional error below 0.3 mm across 50 trials, and voltage supply stability within +/-0.2 V under full system load. End-to-end computation was validated against a parallel software simulator (tlang), with all hardware outputs matching the simulated reference exactly across multiple test programs. The system advances prior physical Turing Machine demonstrations through autonomous execution, reprogrammable optical input, and quantitative evaluation of its mechanical, optical, and computational performance.
